The Role of Advanced Glycation End-Products in the Pathophysiology and Pharmacotherapy of Cardiovascular Disease
Bibliographic record
Abstract
Advanced glycation end-products (AGEs) are formed by the non-enzymatic glycation of proteins, lipids, and nucleic acids due to the consumption of high-carbohydrate diets; their production is also promoted by a sedentary lifestyle as well as cigarette smoking. Elevated levels of AGEs in the circulatory system and internal organs of the body are commonly observed in a number of cardiovascular diseases such as hypertension, diabetes, atherosclerosis, coronary artery disease, aortic aneurysm, atrial fibrillation, myocardial infarction, and heart failure, which are associated with the development of oxidative stress and myocardial inflammation. The adverse effects of AGEs on the cardiovascular system are elicited by both non-receptor mechanisms involving the cross-linking of extracellular and intracellular proteins, and by receptor-mediated mechanisms involving the binding of AGEs with advanced glycation end-product receptors (RAGEs) on the cell membrane. AGE–RAGE interactions along with the cross-linking of proteins promote the generation of oxidative stress, the production of inflammation, the occurrence of intracellular Ca2+-overload, and alterations in the extracellular matrix leading to the development of cardiovascular dysfunction. AGEs also bind with two other protein receptors in the circulatory system: soluble RAGEs (sRAGEs) are released upon the proteolysis of RAGEs due to the activation of matrix metalloproteinase, and endogenous secretory RAGEs (esRAGEs) are secreted as a spliced variant of endogenous RAGEs. While the AGE–RAGE signal transduction axis serves as a pathogenic mechanism, both sRAGEs and esRAGEs serve as cytoprotective interventions. The serum levels of sRAGEs are decreased in ischemic heart disease, vascular disease, and heart failure, as well as in other cardiovascular diseases, but are increased in chronic diabetes and renal disease. Several interventions which can reduce the formation of AGEs, block the AGE–RAGE axis, or increase the levels of circulating sRAGEs have been shown to exert beneficial effects in diverse cardiovascular diseases. These observations support the view that the AGE–RAGE axis not only plays a critical role in pathogenesis, but is also an excellent target for the treatment of cardiovascular disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".